Deciding an optimal action in consideration of risk

ABSTRACT

A method and system for deciding an optimal action in consideration of risk. The method includes the steps of: generating sequentially, by way of a Markov decision process based on a Monte Carlo method, a series of data having states on a memory of a computer; computing a risk measure of a present data by tracking generated data from opposite order to generation order, where the risk measure is calculated from a value at risk or an exceedance probability that is derived from risk measures of a plurality of states transitionable from a state of the present data; and executing the step of computing the risk measure while tracking back to starting data, where at least one of the steps is carried out using a computer device.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority under 35 U.S.C. §119 from JapanesePatent Application No. 2011-029660 filed Feb. 15, 2011, the entirecontents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a technique of deciding an optimalaction in consideration of risk. More specifically, the presentinvention relates to a technique of deciding an action using Markovdecision process (MDP).

2. Description of Related Art

A simulation system and simulation method of integrally evaluatinginterest risk and credit risk of a portfolio are described in JapaneseUnexamined Patent Publication No. 2002-230280. The technique providesthat: (1) a large number of scenarios from a present time to a riskhorizon are generated based on a default-free interest process model anda default process model; (2) a price of a portfolio and a price of anindividual asset in the risk horizon are computed for each of thegenerated scenarios; and (3) a future price distribution of theportfolio and/or a future price distribution of the individual asset aredetermined based on the computed prices. As a result, the techniqueintegrally evaluates interest risk and credit risk of the portfolio.

Research is also conducted on a risk computation technique that uses aMarkov process. The Markov process has a Markov property, where itsfuture state transition depends only on its present state andindependently of its past state. Research is further conducted on anaction decision technique that uses a Markov decision process, which isan extension of the Markov process. For example, for a target capable ofundergoing state transitions, a Markov decision process problem is aproblem to find a rule for deciding an action to be executed in eachstate in order to maximize an expected cumulative reward obtained fromthe target.

To provide a credit portfolio control method used for selecting anoptimal policy in credit control to enable situations of externalfactors such as an economic environment and a set credit line to bereflected on a future credit rating transition probability, JapanesePatent No. 4400837 discloses a technique of creating a graph in whichtransitions of combinations of each state of an existing credit and eachstate of an external factor from the first to T-th years arerepresented. The technique provides: (1) for the first year, a nodeincluding an existing credit's initial state and the external factor'sinitial state and; and (2) for the second to T-th years, nodesindicating patterns of combinations of each state of the existing creditand each state of the external factor. The aforementioned techniquecorresponds to finding an optimal policy that, by way of solving aMarkov decision process problem of T years by dynamic programming (DP),maximizes an expected total gain for T years while tracking back from aterminal T-th year node.

In addition, an iterated risk measure is recently receiving attention asa risk measure based on which a financial institution determines itscapital. A (conditional) value at risk is also called a CTE (conditionaltail expectation), but has no time consistency. However, the iteratedrisk measure has time consistency. This is described in M. R. Hardy andJ. L. Wirch, “The iterated CTE: A dynamic risk measure”, The NorthAmerican Actuarial Journal, 62-75, 2004.

However, a backward-computed iterated CTE (ICTE) is considered to bedifficult to implement, because ICTE requires a large computation load.Furthermore, a typical Monte Carlo method cannot handle ICTE.

The iterated risk measure can represent risk preference that is rationalbut cannot be represented by expected utility, discounted expectedutility, or the like. Accordingly, Japanese Patent Application No.2010-211588 discloses a technique of optimizing a Markov decisionprocess so as to minimize the iterated risk measure using dynamicprogramming.

However, the technique described in the specification of Japanese PatentApplication No. 2010-211588 requires an extremely long computation timewhen the number of possible states or actions increases. Thus, thetechnique can actually solve only limited problems, and as a result thetechnique is constrained.

SUMMARY OF THE INVENTION

Accordingly, one aspect of the present invention provides a method forcomputing an iterated risk measure, the method including the steps of:generating sequentially, by way of a Markov decision process based on aMonte Carlo method, a series of data having states on a memory of acomputer; computing a risk measure of a present data by trackinggenerated data from opposite order to generation order, where the riskmeasure is calculated from a value at risk or an exceedance probabilitythat is derived from risk measures of a plurality of statestransitionable from a state of the present data; and executing the stepof computing the risk measure while tracking back to starting data,where at least one of the steps is carried out using a computer device.

Another aspect of the present invention provides a method for computingan action that minimizes an iterated risk measure, the method includingthe steps of: generating, during postdecision, data includingcombinations of a predetermined state and a possible action on a memoryof the computer; selecting a state-action combination data fromgenerated data of the combinations of the state and the action, based ona value associated with each of the combinations; generating, duringpredecision, a state from selected state-action combination data, by wayof a Markov decision process based on a Monte Carlo method; generating astate data sequence by iterating the step of generating a state and thestep of generating data including combinations; computing, based on riskmeasures of a plurality of states transitionable from a presentpredecision state, a risk measure of an immediately precedingpostdecision state by tracking generated states in opposite order toorder of the generation, where the risk measure is calculated from avalue at risk or an exceedance probability; and setting a value of astate having a minimum value in a present postdecision state to animmediately preceding predecision state, by tracking the generatedstates in the opposite order to the order of the generation, where atleast one of the steps is carried out using a computer device.

Another aspect of the present invention provides a system for computingan iterated risk measure, the system including: a generating module forgenerating sequentially, by way of a Markov decision process based on aMonte Carlo method, a series of data having states on a memory of acomputer; a risk measure module for computing a risk measure of apresent object by tracking generated data from opposite order togeneration order, where the risk measure is calculated from a value atrisk or an exceedance probability that is derived from risk measures ofa plurality of states transitionable from a state of the present object;and an executing module for executing the risk measure module whiletracking back to starting object.

Another aspect of the present invention provides a system for computingan action that minimizes an iterated risk measure, the system including:a postdecision module for generating, during postdecision, dataincluding combinations of a predetermined state and a possible action ona memory of the computer; a selecting module for selecting astate-action combination data from generated data of the combinations ofthe state and the action, based on a value associated with each of thecombinations; a predecision module for generating, during predecision, astate from selected state-action combination data, by way of a Markovdecision process based on a Monte Carlo method; a state data sequencemodule for generating a state data sequence by iterating the step ofgenerating a state and the step of generating data includingcombinations; a risk measure module for computing, based on riskmeasures of a plurality of states transitionable from a presentpredecision state, a risk measure of an immediately precedingpostdecision state by tracking generated states in opposite order toorder of the generation, where the risk measure is calculated from avalue at risk or an exceedance probability; and a value module forsetting a value of a state having a minimum value in a presentpostdecision state to an immediately preceding predecision state, bytracking the generated states in the opposite order to the order of thegeneration.

According to the present invention, it becomes possible to provide atechnique of approximately obtaining an iterated risk measure at highspeed using a probabilistic method such as a Monte Carlo method, where atypical iterated risk measure normally requires considerable time whenprecisely computed.

It is also possible to provide a technique of obtaining an actionsequence that minimizes an iterated risk measure at high speed, usingthe above-mentioned technique of approximately obtaining an iteratedrisk measure at high speed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a hardware structure as an example forimplementing the present invention.

FIG. 2 is a functional block diagram of a logical structure for aprocess of computing an iterated risk measure according to an embodimentof the present invention.

FIG. 3 is a flowchart of the process of computing an iterated riskmeasure according to an embodiment of the present invention.

FIG. 4 is a flowchart of a process of a SAMPLE_POLICY routine in theprocess of computing an iterated risk measure.

FIG. 5 is a diagram showing correspondence between states and reachingprobabilities referenced to in the SAMPLE_POLICY routine.

FIG. 6 is a flowchart of a process of an UPDATE_VALUE routine in theprocess of computing an iterated risk measure.

FIG. 7 is a diagram showing correspondence between states, values, andreaching probabilities referenced to in the UPDATE_VALUE routine.

FIG. 8 is a diagram schematically showing the process of computing aniterated risk measure.

FIG. 9 is a functional block diagram of a logical structure for aprocess of deciding an action that minimizes an iterated risk measureaccording to the present invention.

FIG. 10 is a flowchart of the process of deciding an action thatminimizes an iterated risk measure according to the present invention.

FIG. 11 is a flowchart of a process of an EXPLORATION_POLICY routine inthe process of deciding an action that minimizes an iterated riskmeasure.

FIG. 12 is a diagram showing correspondence between postdecision states,values, and counters referenced to in the EXPLORATION_POLICY routine.

FIG. 13 is a flowchart of a process of an UPDATE_VALUE_MIN routine inthe process of deciding an action that minimizes an iterated riskmeasure.

FIG. 14 is a diagram showing correspondence between postdecision statesand values referenced to in the UPDATE_VALUE_MIN routine.

FIG. 15 is a diagram schematically showing the process of deciding anaction that minimizes an iterated risk measure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following describes an embodiment of the present invention based ondrawings. The same reference numerals designate the same elementsthroughout the drawings, unless otherwise stated. Note that thefollowing merely describes one embodiment of the present invention, andthe scope of the present invention is not limited to this embodiment.

It is an object of the present invention to provide a technique ofcomputing an iterated risk measure at high speed using a Monte Carlomethod, in a Markov process.

It is another object of the present invention to provide a technique ofapproximately deciding an action that minimizes an iterated risk measureby applying the above-mentioned technique of computing an iterated riskmeasure at high speed, in a Markov decision process of such a size thatcannot be precisely optimized.

In a first aspect of the present invention, a state sequence (S1, S2, .. . , Sn) is generated by sequential sampling based on a Markov process,by processing of a computer. A (iterated risk measure provisional) value(V(Sn), . . . , V(S2), V(S1)) of each state is then updated in oppositeorder (Sn, . . . , S2, S1) to the generation order.

A value V(Si) of each state Si is updated according to a risk measure(especially computed using a value at risk or an exceedance probabilityor partially using them) of a random variable defined from a transitionprobability p(Si+1(j)|Si) to a state (Si+1(1), Si+1(2), . . . , Si+1(m))reachable from the state by one transition and a value V(Si+1(j)) of thetransition destination. Iterating this process of a predeterminedduration yields an iterated risk measure approximate value. Hereafter,the iterated risk measure provisional value is also simply referred toas a “value”.

In a second aspect of the present invention, a technique ofapproximately deciding an action that minimizes an iterated risk measurein a specific state through the use of the above-mentioned technique ofapproximately computing a risk measure is provided. In this technique,states are generated so that a predecision state and a postdecisionstate appear alternately in the above-mentioned technique of the firstaspect.

A (iterated risk measure provisional) value of a postdecision state iscomputed using a value of a next reachable predecision state, as in theabove-mentioned technique of the first aspect. A (iterated risk measureprovisional) value of a predecision state is updated using a minimumiterated risk measure provisional value of a next reachable postdecisionstate. As a result of iteration, an action sequence that minimizes aniterated risk measure is selected.

FIG. 1 is a block diagram of computer hardware for realizing a systemstructure and process according to an embodiment of the presentinvention. In FIG. 1, a CPU 104, a main memory (RAM) 106, a hard diskdrive (HDD) 108, a keyboard 110, a mouse 112, and a display 114 areconnected to a system path 102. The CPU 104 is preferably based on a32-bit or 64-bit architecture. For example, Pentium™ 4, Core™ 2 Duo, orXeon™ by Intel Corporation, Athlon™ by AMD, or the like can be used forthe CPU 104. The main memory 106 preferably has a capacity of 4 GB ormore. The hard disk drive 108 desirably has a capacity of, for example,500 GB or more, to allow a large amount of data to be stored.

The hard disk drive 108 stores an operating system beforehand, thoughnot shown. The operating system can be an arbitrary operating systemcompatible with the CPU 104, such as Linux™, Windows XP™ or Windows™ 7by Microsoft Corporation, or Mac OS™ by Apple Inc.

The hard disk drive 108 also stores data and parameters for probabilitycomputation of a Markov decision process, processing routines for theprocess according to the present invention, and so on. These parametersand processing routines will be described in detail later, withreference to FIG. 2.

The keyboard 110 and the mouse 112 are used to activate the operatingsystem or a program (not shown) which is loaded from the hard disk drive108 into the main memory 106 and displayed on the display 114, or entercharacters.

The display 114 is preferably a liquid crystal display, and can have,for example, an arbitrary resolution such as XGA (1024×768 inresolution) or UXGA (1600×1200 in resolution). The display 114 is usedto display an operation window for starting the process according to thepresent invention, a computation result of a selected action, risk, andthe like, though not shown.

The following describes processing routines for executing especially aprocess of approximately computing an iterated risk measure according tothe present invention, with reference to a functional block diagram inFIG. 2. These processing routines are generated in an existingprogramming language such as C, C++, or Java® beforehand, held in thehard disk drive 108 in an executable form, and loaded into the mainmemory 106 and executed according to the operating system.

In this embodiment, a process of selecting a stock in which a user is toinvest in each term with predetermined money in possession is describedas the process according to the present invention, though the presentinvention is not limited to such. The following scenario is assumed. Astock in which the user is to invest in each term is selected, startingfrom predetermined money in possession. A state is represented by acombination of (money in possession, stock in which the user invests,time). There are action candidates as many as stock types. In each term,there are action candidates as many as stock types, and which stock theuser is to invest in is decided. A return as a result of taking anaction in each state is determined by a return for a period of acorresponding stock.

A main routine 202 is a program for an overall operation according tothe present invention, and has a function of displaying an operationwindow on the display 114, receiving a user operation and starting aprocess, and the like, though not shown.

A parameter 204 includes parameters and data for computing probabilityof a Markov decision process indicating performance of various stocks,and the like.

A SAMPLE_POLICY routine 206 is a routine for performing a process ofgenerating a state with a predetermined probability by a generatedrandom number, according to a Monte Carlo method.

An UPDATE_VALUE routine 208 is a routine for computing a risk measure byreferencing to a set of directly transitionable states.

An output routine 210 is a routine for outputting a risk value as acomputation result. The computation result is displayed on the display114 according to need.

The following describes the process of approximately computing aniterated risk measure according to the present invention, with referenceto a flowchart in FIG. 3. For example, this process is started by anoperator operating a menu of a window screen displayed on the display114 using the keyboard 110 or the mouse 112.

In this embodiment, it is assumed that a series of data objects forstoring states are already loaded into the main memory 106 prior to theprocess described below. The data objects are, for example, instances ofa class in Java® or C++. FIG. 8 schematically shows such data objects. Aseries of data objects 802, 804, 806, and 808 is shown in FIG. 8.

In step 302, the main routine 202 sets an initial value of a variable sindicating a state, from the parameter 204. The variable s is set to anattribute value of the data object 802 which is the first data object inFIG. 8. For example, the state is represented by a combination of (moneyin possession, stock in which the user invests, time).

In step 304, the main routine 202 pushes the state s onto a stack. Suchpushing the state s onto the stack is performed for later popping andbacktracking of the state.

Next, in step 306, the main routine 202 calls the SAMPLE_POLICY routine206 by SAMPLE_POLICY(s) using s as an argument.

FIG. 4 shows a detailed process of the SAMPLE_POLICY routine 206. InFIG. 4, the SAMPLE_POLICY routine 206 generates, for i=1, . . . , n, arandom number so that i occurs with a probability p_(i), in step 402.The generated random number is denoted by m (1≦m≦n). The probabilityp_(i) mentioned here is a probability of transiting from the state s toa state s_(i), in a Markov process context.

FIG. 5 shows a transition probability of each state s_(i) from s. Suchcorrespondence information is prepared beforehand for each different s,in the parameter 204.

The SAMPLE_POLICY routine 206 outputs a state s_(m) corresponding to therandom number m in step 404.

Returning to step 306 in FIG. 3, such a returned value s_(m) is assignedto s. This corresponds to a situation where a transition is made to astate S2 of the data object 804 in FIG. 8.

In step 308, the main routine 202 pushes the state s onto the stack. Instep 310, the main routine 202 determines whether or not to stop forwardsampling. A criterion for stopping forward sampling is, for example,whether or not states are generated for a predetermined number ofstages. In the example in FIG. 8, the state 802 is the first stage, thestate 804 is the second stage, the state 806 is the third stage, and thestate 808 is the fourth stage. Alternatively, the criterion for stoppingforward sampling can be whether or not a predetermined time elapses fromthe start of the process.

In the case where the main routine 202 determines that the criterion forstopping forward sampling is not met, the main routine 202 returns tostep 306 and calls the SAMPLE_POLICY routine 206.

On the other hand, in the case where the main routine 202 determinesthat the criterion for stopping forward sampling is met in step 310, themain routine 202 goes to step 312, and pops the state s from the stack.

Next, in step 314, the main routine 202 calls the UPDATE_VALUE routine208 by UPDATE_VALUE(s).

FIG. 6 shows a detailed process of the UPDATE_VALUE routine 208. Step602 is a definition block. In step 602, the UPDATE_VALUE routine 208sets {s₁, s₂, . . . , s_(n)} as a set of states directly transitionablefrom s (i.e. having a transition probability more than 0), where n isthe number of directly transitionable states from s. In the example inFIG. 8, states 806 a, 806 b, and 806 c are directly transitionablestates from the state S2 designated by reference numeral 804 c. TheUPDATE_VALUE routine 208 also sets, for i=1, . . . , n, p_(i) as aprobability of transitioning from s to s_(i), and v _(i) as a value(iterated risk measure provisional value) of s_(i). FIG. 7 shows thiscorrespondence. In FIG. 7, the fields of the state and the reachingprobability from s are based on values stored in the parameter 204beforehand, but the field of the value can initially store 0. This beingthe case, values are sequentially stored as a result of computation.Alternatively, the value can be initially set as the money in possessionin the state. The present invention can be realized with other initialvalue settings.

In step 604, the UPDATE_VALUE routine 208 computes, for i=1, . . . , n,a α% value at risk of a random variable X that takes the value v_(i)with the probability p_(i), according to the following expression. Thecomputation result is denoted by V_(α).

$\begin{matrix}{{{VaR}_{\alpha \%}\lbrack X\rbrack} = {\inf\limits_{x \in R}\left\{ {{\sum\limits_{{i\text{:}\upsilon_{i}} > x}^{\;}p_{i}} \leq {1 - \frac{\alpha}{100}}} \right\}}} & \left\lbrack {{Math}.\mspace{14mu} 1} \right\rbrack\end{matrix}$

An exceedance probability can be computed instead of V_(α), according tothe following expression.

$\begin{matrix}{{\Pr \left( {X > x} \right)} = {\sum\limits_{{i\text{:}\upsilon_{i}} > x}^{\;}p_{i}}} & \left\lbrack {{Math}.\mspace{14mu} 2} \right\rbrack\end{matrix}$

In step 606, the UPDATE_VALUE routine 208 computes a risk measure v ofX, using V_(α) or the exceedance probability. For example, thiscomputation is performed as v=E[X|X>V_(α)]. As an alternative, the riskmeasure v can be computed by the following expression partially usingthe exceedance probability.

p _(n) [Y]=E[Y]−c(Pr(Y≦0)−α)I{Pr(Y≦0)≧α}  [Math. 3]

In this expression, I{ } is a function that returns 1 when theexpression in { } is true, and 0 when the expression in { } is false.

In step 608, the UPDATE_VALUE routine 208 stores the computed v as avalue corresponding to the state s. In the example in FIG. 8, the riskmeasure v computed based on the states 806 a, 806 b, and 806 c is storedin association with the state S2.

Returning to the process of the flowchart in FIG. 3, after step 314 ofcalling UPDATE_VALUE(s), the main routine 202 determines whether or notthe stack is empty in step 316. In the case where the stack is notempty, the main routine 202 returns to step 312.

In the case where the main routine 202 determines that the stack isempty in step 316, the main routine 202 determines whether or not astopping condition is met in step 318. The stopping condition mentionedhere is any of whether or not a predetermined time elapses from thestart of the process shown in the flowchart in FIG. 3, whether or not aloop of steps 302 to 318 is performed a predetermined number of times,or whether or not a risk measure computed value at the starting pointdesignated by S1 in FIG. 8 eventually has only a change of a thresholdor below from a value computed in the immediately preceding loop ofsteps 302 to 318, though the present invention is not limited to such.

In the case where the main routine 202 determines that the stoppingcondition is not met in step 318, the main routine 202 returns to step302, to resume computation from the first state S1 in FIG. 8. At thistime, the previously computed risk measure values (the values in thevalue field in FIG. 7) are maintained, so that the intermediate riskmeasure values which were initially mostly 0 are gradually changed tononzero values as the loop of steps 302 to 318 is repeated.

In the case where the main routine 202 determines that the stoppingcondition is met in step 318, the process ends, and the output routine210 outputs a risk measure value corresponding to the first state S1 inFIG. 8.

The following describes processing routines for executing a process ofapproximately deciding an action that minimizes an iterated risk measurein a specific state according to the present invention, with referenceto a functional block diagram in FIG. 9. These processing routines arealso generated in an existing programming language such as C, C++, orJava® beforehand, held in the hard disk drive 108 in an executable form,and loaded into the main memory 106 and executed according to theoperating system.

The process of approximately deciding an action that minimizes aniterated risk measure uses the routine for approximately computing aniterated risk measure shown in FIG. 2, and so there are some commonprocessing routines. However, the processing routines in FIG. 9 aregiven different reference numerals from those in FIG. 2.

In this embodiment, too, a process of selecting a stock in which theuser is to invest in each term with predetermined money in possession isdescribed as the process according to the present invention. Thefollowing scenario is assumed. A stock in which the user is to invest ineach term is selected, starting from predetermined money in possession.A state is represented by a combination of (money in possession, stockin which the user invests, time). There are action candidates as many asstock types. In each term, there are action candidates as many as stocktypes, and which stock the user is to invest in is decided. A return asa result of taking an action in each state is determined by a return fora period of a corresponding stock.

A main routine 902 is a program for an overall operation according tothe present invention, and has a function of displaying an operationwindow on the display 114, receiving a user operation and starting aprocess, and the like, though not shown.

A parameter 904 includes parameters and data for computing probabilityof a Markov decision process indicating performance of various stocks,and the like.

A SAMPLE_POLICY routine 906 is a routine for performing a process ofgenerating a state with a predetermined probability by a generatedrandom number, according to a Monte Carlo method. The SAMPLE_POLICYroutine 906 can be the same as the SAMPLE_POLICY routine 206 in FIG. 2.

An EXPLORATION_POLICY routine 908 is a routine for selecting apostdecision state.

An UPDATE_VALUE routine 910 is a routine for computing a risk measure byreferencing to a set of directly transitionable states. The UPDATE_VALUEroutine 910 can be the same as the UPDATE_VALUE routine 208 in FIG. 2.

An UPDATE_VALUE_MIN routine 912 is a routine for returning a minimumvalue in the set of directly transitionable states.

An output routine 914 is a routine for outputting an action sequence asa computation result. The computation result is displayed on the display114 according to need.

The following describes the process of approximately deciding an actionthat minimizes an iterated risk measure according to the presentinvention, with reference to a flowchart in FIG. 10. For example, thisprocess is started by an operator operating a menu of a window screendisplayed on the display 114 using the keyboard 110 or the mouse 112.

In this embodiment, it is assumed that a series of data objects forstoring states are already loaded into the main memory 106 prior to theprocess described below. The data objects are, for example, instances ofa class in Java® or C++. FIG. 15 schematically shows such data objects.A series of data objects 1502, 1504, 1506, 1508, 1510, 1512, 1514, and1516 is shown in FIG. 15. In this embodiment, two states that are apredecision state and a postdecision state are used. In FIG. 15, thedata objects 1502, 1506, 1510, and 1514 correspond to predecisionstates, and the data objects 1504, 1508, 1512, and 1516 correspond topostdecision states.

In step 1002, the main routine 902 sets an initial value of a variable sindicating a state, from the held parameter 904. The variable s is setto an attribute value of the data object 1502 which is the first dataobject in FIG. 15 and corresponds to a predecision state. For example,the state is represented by a combination of (money in possession, stockin which the user invests, time).

In step 1004, the main routine 902 pushes the state s onto a stack. Suchpushing the state s onto the stack is performed for later popping andbacktracking of the state.

In step 1006, the main routine 902 calls the EXPLORATION_POLICY routine908 by EXPLORATION_POLICY(s) using s as an argument.

FIG. 11 shows a detailed process of the EXPLORATION_POLICY routine 908.As shown in definition step 1102, the EXPLORATION_POLICY routine 908sets {a₁, a₂, . . . , a_(n)} as a set of actions that can be taken inthe state s. The EXPLORATION_POLICY routine 908 also sets, for i=1, . .. , n, s′_(i)=(s, a_(i)) as a postdecision state when a_(i) is taken ins, v_(i) as a value of s′_(i), and c _(i) as the number of visits tos′_(i). The value mentioned here is the same as that described withreference to FIG. 7. The number of visits to s′_(i) is denoted by c_(i).The number of visits c_(i) is recorded in order to select a balancedaction sequence by avoiding a postdecision state with a large number ofvisits as much as possible. FIG. 12 shows an example of correspondencebetween postdecision states, values, and counters.

In step 1104, the EXPLORATION_POLICY routine 908 computes i thatminimizes a function f(v_(i), c_(i)).

For example, the function f is an expression such as f(v,c)≡v+α(β/c)^(0.6) though the present invention is not limited to such.That is, the function f has a requirement of monotonically increasingwith v and monotonically decreasing with c. α and β are positiveconstants, and parameters that can be arbitrarily set.

The EXPLORATION_POLICY routine 908 sets the computed i as i* in step1104. The EXPLORATION_POLICY routine 908 increments c_(i)* asc_(i)*=c_(i)*+1 in step 1106, and outputs s_(i)* in step 1108.

The output of s_(i)* can be understood more easily with reference toFIG. 15. Though postdecision states 1504 a, 1504 b, and 1504 c can bereached from the predecision state 1502 by possible different actions,the postdecision state 1504 c is selected according to the computationin step 1104.

Returning to step 1006 in FIG. 10, after the EXPLORATION_POLICY routine908 is completed and s′ is output in step 1006, the main routine 902pushes s′ onto the stack in step 1008.

Next, in step 1010, the main routine 902 calls the SAMPLE_POLICY routine906 by SAMPLE_POLICY(s′) using s′ as an argument.

The SAMPLE_POLICY routine 906 performs a process of selecting onetransitionable state based on the combination of a Monte Carlo methodand a Markov decision process, in the same manner as the SAMPLE_POLICYroutine 206. Since this process is the same as that shown in theflowchart in FIG. 4, its description is omitted here. This stateselection corresponds to selecting a state 1506 b in the predecisionstate 1506 from the state (S1) 1504 c in the postdecision state 1504 inFIG. 15.

After the SAMPLE_POLICY routine 906 selects s from s′ in step 1010, themain routine 902 pushes s onto the stack in step 1012.

Next, in step 1014, the main routine 902 determines whether or not tostop forward sampling. A criterion for stopping forward sampling is, forexample, whether or not states are generated for a predetermined numberof stages. Alternatively, the criterion for stopping forward samplingcan be whether or not a predetermined time elapses from the start of theprocess.

In the case where the main routine 902 determines that the criterion forstopping forward sampling is not met, the main routine 902 returns tostep 1006 to call the EXPLORATION_POLICY routine 908.

On the other hand, in the case where the main routine 902 determinesthat the criterion for stopping forward sampling is met in step 1014,the main routine 902 goes to step 1016, and pops the state s from thestack.

Next, the main routine 902 calls the UPDATE_VALUE_MIN routine 912 byUPDATE_VALUE_MIN(s) using the popped state s. The following describes aprocess of the UPDATE_VALUE_MIN routine 912, with reference to aflowchart in FIG. 13.

In FIG. 13, step 1302 is a definition step. In step 1302, theUPDATE_VALUE_MIN routine 912 sets {s′₁, s′₂, . . . , s′_(n)} as a set ofpostdecision states directly reachable from s. The UPDATE_VALUE_MINroutine 912 also sets, for i=1, . . . , n, v_(i) as a value of s′_(i).FIG. 14 shows correspondence between postdecision states and values.

In next step 1304, the UPDATE_VALUE_MIN routine 912 computes, for i=1, .. . , n, a minimum value of v_(i) as v, according to v=min_(i) v_(i). Instep 1306, the UPDATE_VALUE_MIN routine 912 stores the computed v as avalue of s. In the example in FIG. 15, supposing that the popped state sis a predecision state (S4) 1514 c, actions 1516 a, 1516 b, and 1516 care actions that can be taken in the state 1514 c. When the minimumvalue among the values associated with the actions 1516 a, 1516 b, and1516 c is the value associated with the action 1516 c, this value isstored in the state 1514 c.

Returning to the flowchart in FIG. 10, the main routine 902 determineswhether or not the stack is empty in step 1020. In the case where thestack is empty, the main routine 902 determines whether or not astopping condition is met in step 1026. The stopping condition mentionedhere is any of whether or not a predetermined time elapses from thestart of the process shown in the flowchart in FIG. 10, whether or not aloop of steps 1002 to 1014 is performed a predetermined number of times,or whether or not a value at the starting point designated by S1 in FIG.15 eventually has only a change of a threshold or below from a valuecomputed in the immediately preceding loop of steps 1002 to 1014.

In the case where the main routine 902 determines that the stoppingcondition is met, the process ends. Otherwise, the main routine 902returns to step 1002, to resume the process from the first step. At thistime, the values set in the states in the previous loop are maintained,and put to use in the next computation.

In the case where the main routine 902 determines that the stack is notempty in step 1020, the main routine 902 pops the state s′ from thestack in step 1022. The main routine 902 then calls the UPDATE_VALUEroutine 910 by UPDATE_VALUE(s′) in step 1024, to update the value of s′.The process of the UPDATE_VALUE routine 910 is substantially the same asthe process of the UPDATE_VALUE routine 208, which is shown in detail inthe flowchart in FIG. 6. In the example in FIG. 15, a value of apostdecision state (S3) 1512 b is computed from predecision states 1514a, 1514 b, and 1514 c.

After step 1024, the main routine 902 returns to step 1016. As a result,an action sequence of actions 1504 c, 1508 a, 1512 b, and 1516 c isobtained. The main routine 902 calls the output routine 914 to output,for each predecision state, an action a associated with a postdecisionstate (s, a) having a minimum value among postdecision states directlytransitionable from the predecision state. The output result ispreferably displayed on the display 114 or written to a file.

Though the above embodiment of the present invention is described usingan example of applying to a process of selecting a stock in which theuser is to invest in each term with predetermined money in possession,the present invention is not limited to this, and is applicable to anydecision making process that involves probabilistic risk computationperformed sequentially in time series.

The present invention is not limited to a specific hardware and softwareplatform of a computer, and can be implemented with any platform.

The above and other features of the present invention will become moredistinct by a detailed description of embodiments shown in combinationwith attached drawings. Identical reference numbers represent the sameor similar parts in the attached drawings of the invention.

As will be appreciated by one skilled in the art, aspects of the presentinvention can be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention can take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that can allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention can take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) can beutilized. A computer readable storage medium can be, for example, butnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, or device, or any suitablecombination of the foregoing. More specific examples (a non-exhaustivelist) of the computer readable storage medium can include the following:an electrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium can be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

Computer program code for carrying out operations for aspects of thepresent invention can be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code can execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer.

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions canbe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions can also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions can also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof code, which includes one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock can occur out of the order noted in the figures. For example, twoblocks shown in succession can, in fact, be executed substantiallyconcurrently, or the blocks can sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “includes”and/or “including,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A method for computing an iterated risk measure, the methodcomprising the steps of: generating sequentially, by way of a Markovdecision process based on a Monte Carlo method, a series of data havingstates on a memory of a computer; computing a risk measure of a presentdata by tracking generated data from opposite order to generation order,wherein said risk measure is calculated from a value at risk or anexceedance probability that is derived from risk measures of a pluralityof states transitionable from a state of said present data; andexecuting said step of computing said risk measure while tracking backto starting data, wherein at least one of the steps is carried out usinga computer device.
 2. The method according to claim 1, wherein said stepof computing a risk measure uses a following expression:${{VaR}_{\alpha \%}\lbrack X\rbrack} = {\inf\limits_{x \in R}\left\{ {{\sum\limits_{{i\text{:}\upsilon_{i}} > x}^{\;}p_{i}} \leq {1 - \frac{\alpha}{100}}} \right\}}$wherein: X is a random variable; v_(i) (i=1, . . . , n) is a value ofeach of said plurality of states transitionable from said state of saidpresent data and p_(i) (i=1, . . . , n) is a transition probability ofeach of said plurality of states transitionable from said state of saidpresent data.
 3. The method according to claim 1, wherein said step ofcomputing a risk measure uses a following expression:${\Pr \left( {X > x} \right)} = {\sum\limits_{{i\text{:}\upsilon_{i}} > x}^{\;}p_{i}}$wherein: Pr is a probability; X is a random variable; v_(i) (i=1, . . ., n) is a value of each of said plurality of states transitionable fromsaid state of a present object; and p_(i) (i=1, . . . , n) is atransition probability of each of said plurality of statestransitionable from said state of said present object.
 4. A computerreadable storage medium tangibly embodying a computer readable programcode having computer readable instructions which when implemented, causea computer to carry out the steps of claim
 1. 5. A method for computingan action that minimizes an iterated risk measure, the method comprisingthe steps of: generating, during postdecision, data comprisingcombinations of a predetermined state and a possible action on a memoryof the computer; selecting a state-action combination data fromgenerated data of said combinations of said state and said action, basedon a value associated with each of said combinations; generating, duringpredecision, a state from selected state-action combination data, by wayof a Markov decision process based on a Monte Carlo method; generating astate data sequence by iterating said step of generating a state andsaid step of generating data comprising combinations; computing, basedon risk measures of a plurality of states transitionable from a presentpredecision state, a risk measure of an immediately precedingpostdecision state by tracking generated states in opposite order toorder of the generation, wherein said risk measure is calculated from avalue at risk or an exceedance probability; and setting a value of astate having a minimum value in a present postdecision state to animmediately preceding predecision state, by tracking said generatedstates in the opposite order to the order of the generation, wherein atleast one of the steps is carried out using a computer device.
 6. Themethod according to claim 5, wherein the step of computing a riskmeasure uses the following expression:${{VaR}_{\alpha \%}\lbrack X\rbrack} = {\inf\limits_{x \in R}\left\{ {{\sum\limits_{{i\text{:}\upsilon_{i}} > x}^{\;}p_{i}} \leq {1 - \frac{\alpha}{100}}} \right\}}$wherein: X is a random variable; v_(i) (i=1, . . . , n) is a value ofeach of said plurality of states transitionable from said state of apresent object; and p_(i) (i=1, . . . , n) is a transition probabilityof each of said plurality of states transitionable from said state ofsaid present object.
 7. The method according to claim 5, wherein thestep of computing a risk measure uses the following expression:${\Pr \left( {X > x} \right)} = {\sum\limits_{{i\text{:}\upsilon_{i}} > x}^{\;}p_{i}}$wherein: Pr is a probability; X is a random variable; v_(i) (i=1, . . ., n) is a value of each of said plurality of states transitionable fromsaid state of a present object; and p_(i) (i=1, . . . , n) is atransition probability of each of said plurality of statestransitionable from said state of said present object.
 8. The methodaccording to claim 5, wherein said step of selecting a state-actioncombination data uses an evaluation function which is a monotonicallydecreasing function with respect to a frequency of visiting said state.9. A system for computing an iterated risk measure, the systemcomprising: a generating module for generating sequentially, by way of aMarkov decision process based on a Monte Carlo method, a series of datahaving states on a memory of a computer; a risk measure module forcomputing a risk measure of a present object by tracking generated datafrom opposite order to generation order, wherein said risk measure iscalculated from a value at risk or an exceedance probability that isderived from risk measures of a plurality of states transitionable froma state of said present object; and an executing module for executingsaid risk measure module while tracking back to starting object.
 10. Thesystem according to claim 9, wherein said risk measure module computessaid risk measure using the following expression:${{VaR}_{\alpha \%}\lbrack X\rbrack} = {\inf\limits_{x \in R}\left\{ {{\sum\limits_{{i\text{:}\upsilon_{i}} > x}^{\;}p_{i}} \leq {1 - \frac{\alpha}{100}}} \right\}}$wherein: X is a random variable; v_(i) (i=1, . . . , n) is a value ofeach of said plurality of states transitionable from said state of apresent object; and p_(i) (i=1, . . . , n) is a transition probabilityof each of said plurality of states transitionable from said state ofsaid present object.
 11. The system according to claim 9, wherein saidrisk measure module computes said risk measure using the followingexpression:${\Pr \left( {X > x} \right)} = {\sum\limits_{{i\text{:}\upsilon_{i}} > x}^{\;}p_{i}}$wherein: Pr is a probability; X is a random variable; v_(i) (i=1, . . ., n) is a value of each of said plurality of states transitionable fromsaid state of a present object; and p_(i) (i=1, . . . , n) is atransition probability of each of said plurality of statestransitionable from said state of said present object.
 12. A computerreadable storage medium tangibly embodying a computer readable programcode having computer readable instructions which when implemented, causea computer to carry out the steps of claim
 5. 13. A system for computingan action that minimizes an iterated risk measure, the systemcomprising: a postdecision module for generating, during postdecision,data comprising combinations of a predetermined state and a possibleaction on a memory of the computer; a selecting module for selecting astate-action combination data from generated data of said combinationsof said state and said action, based on a value associated with each ofsaid combinations; a predecision module for generating, duringpredecision, a state from selected state-action combination data, by wayof a Markov decision process based on a Monte Carlo method; a state datasequence module for generating a state data sequence by iterating saidstep of generating a state and said step of generating data comprisingcombinations; a risk measure module for computing, based on riskmeasures of a plurality of states transitionable from a presentpredecision state, a risk measure of an immediately precedingpostdecision state by tracking generated states in opposite order toorder of the generation, wherein said risk measure is calculated from avalue at risk or an exceedance probability; and a value module forsetting a value of a state having a minimum value in a presentpostdecision state to an immediately preceding predecision state, bytracking said generated states in the opposite order to the order of thegeneration.
 14. The system according to claim 13, wherein said riskmeasure module computes said risk measure using the followingexpression:${{VaR}_{\alpha \%}\lbrack X\rbrack} = {\inf\limits_{x \in R}\left\{ {{\sum\limits_{{i\text{:}\upsilon_{i}} > x}^{\;}p_{i}} \leq {1 - \frac{\alpha}{100}}} \right\}}$wherein: X is a random variable; v_(i) (i=1, . . . , n) is a value ofeach of said plurality of states transitionable from said state of apresent object; and p_(i) (i=1, . . . , n) is a transition probabilityof each of said plurality of states transitionable from said state ofsaid present object.
 15. The system according to claim 13, wherein thesaid risk measure module computes said risk measure using the followingexpression:${\Pr \left( {X > x} \right)} = {\sum\limits_{{i\text{:}\upsilon_{i}} > x}^{\;}p_{i}}$wherein: Pr is a probability; X is a random variable; v_(i) (i=1, . . ., n) is a value of each of said plurality of states transitionable fromsaid state of a present object; and p_(i) (i=1, . . . , n) is atransition probability of each of said plurality of statestransitionable from said state of said present object.
 16. The systemaccording to claim 13, wherein said selecting module uses an evaluationfunction which is a monotonically decreasing function with respect to afrequency of visiting said state.